Gait Recognition Based on Local Graphical Skeleton Descriptor With Pairwise Similarity Network

Gait recognition aims to identify a human through a walking sequence. It is a challenging task in computer vision since monocular camera loses most of the 3D information. Previous works described gait features with the contours of shape or the global geometrical characters of skeleton. So little work is researched on the local patterns of gait skeleton. In this paper, to resist the dress changes and speed changes, a Local Graphical Skeleton Descriptor (LGSD) is proposed to describe both the inner and intra local graphical patterns of a human gait skeleton. The gait features from the same or different identities are paired up and a Pairwise Similarity Network (PSN) is proposed to maximize the similarity of True matched pairs and minimize the similarity of False matched pairs. The contributions of our method are: 1) LGSD is proposed to describe human gait by computing four novel local geometrical patterns of skeleton sequences, which makes use of the intuitive cognition of gait based on the prior knowledge of mankind. 2) PSN is implemented by a two-stream CNN structure to build the gait model, which fused two popular gait recognition strategies. 3) The robustness of our method to dress changes and speed changes is proved on the public datasets. We have also achieved some state-of-the-art results on these datasets. The proposed method is examined on three public gait datasets which have RGB or infrared frames for evaluation: the CASIA-B dataset, the NLPR gait database, and the CASIA-C dataset. The performers in these datasets are walking under different views, speeds or dresses. The results are further compared with previous approaches to confirm the effectiveness and the advantages of our method.